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Direct approach for mining association rules from structured XML data
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An optimal mesh algorithm for remote protein homology detection
Published 2011“…This paper also shows that the use of the refinement algorithm increases the performance of the multiple alignments programs by at least 4%.…”
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Study and Implementation of Data Mining in Urban Gardening
Published 2019“…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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Performance analysis of support vector machine, Gaussian Process Regression, sequential quadratic programming algorithms in modeling hydrogen-rich syngas production from catalyzed...
Published 2022“…Taking advantage of the data generated from the process, this study explores the performance of twelve machine learning algorithms built on the support vector machine (SVM), the Gaussian process regression (GPR), and the non-linear response quadratic model (NLRQM) using Sequential quadratic programming, and the Levenberg-Marquardt algorithms. …”
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Predicting uniaxial compressive strength using Support Vector Machine algorithm
Published 2019“…This paper presents the application of Support Vector Machine (SVM) algorithm to predict the UCS. An algorithm has been tested on a series of rock data using dry density and velocity parameters. …”
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Lightning fault classification for transmission line using support vector machine
Published 2023“…The proposed method was implemented in the MATLAB/SIMULINK programming platform. The classification performance of the developed algorithms was evaluated using confusion matrix. …”
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Mining Sequential Patterns Using I-PrefixSpan
Published 2007“…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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Conference or Workshop Item -
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Performance analysis of support vector machine, Gaussian Process Regression, sequential quadratic programming algorithms in modeling hydrogen-rich syngas production from catalyzed...
Published 2022“…Taking advantage of the data generated from the process, this study explores the performance of twelve machine learning algorithms built on the support vector machine (SVM), the Gaussian process regression (GPR), and the non-linear response quadratic model (NLRQM) using Sequential quadratic programming, and the Levenberg-Marquardt algorithms. …”
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A web-based implementation of k-means algorithms
Published 2022“…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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Final Year Project / Dissertation / Thesis -
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A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem
Published 2016“…The main keys of the new classifier are based on the new kernel method, new learning metric and a new optimization algorithm in order to optimize the SVM decision function. …”
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Thesis -
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Lightning Fault Classification for Transmission Line Using Support Vector Machine
Published 2024“…The proposed method was implemented in the MATLAB/SIMULINK programming platform. The classification performance of the developed algorithms was evaluated using confusion matrix. …”
Conference Paper -
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Mining Sequential Patterns using I-PrefixSpan
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Citation Index Journal -
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Kernel methods and support vector machines for handwriting recognition
Published 2023“…Finding the solution hyperplane involves using quadratic programming which is computationally intensive. …”
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Building extraction of worldview3 imagery via support vector machine using scikit-learn module / Najihah Ismail
Published 2021“…A Commercial Remote Sensing Technology (ENVI) was used and measured to improve and verify the performance of the Python programming-based picture classification by applying the same SVM algorithm and the tests indicated 95.0732% for an overall accuracy.…”
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Implementation Of SVM For Cascaded H-Bridge Multilevel Inverters Utilizing FPGA
Published 2019“…However, the use of multilevel inverters associated with SVM by using Digital Signal Processor (DSP) increases the complexity of control algorithm or computational burden and hence produces larger value of sampling time. …”
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Thesis -
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